Evidence map›Paper›PMID 42231464›Full record

ArticleGenome biology2026

pAnno: a comprehensive, precise, and fast proteogenomic workflow for the discovery of novel coding regions.

Kaifei Wang, Zhuohong Wei, Changrong Li, Yaping Peng, Jiale Zhao, Pengzhi Mao, Ching Tarn, Jinyang Li, Ranfei Chen, Jiaxiang Ding and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Kaifei WangKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Zhuohong WeiKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Changrong LiKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Yaping PengKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Jiale ZhaoKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Pengzhi MaoKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Ching TarnKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Jinyang LiKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Ranfei ChenKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Jiaxiang DingKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China.
Feng GeKey Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China. gefeng@ihb.ac.cn.
Mingkun YangKey Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China. yangmingkun@ihb.ac.cn.
Hao ChiKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing, 100190, China. chihao@ict.ac.cn.

Funding

National Key Research and Development Program of China 2025YFA1309400National Natural Science Foundation of China 32261133523National Natural Science Foundation of China 32471501
6 · The paper itself

Abstract

Proteogenomics is a transformative approach for deciphering novel coding regions through integration of genomic, transcriptomic, and proteomic data. Here, we present pAnno, an end-to-end workflow designed to uncover hidden protein-coding elements with high precision and efficiency. pAnno generates customized protein databases by integrating multi-omic data, employs a multi-stage iterative open search strategy, and incorporates an efficient peptide-to-coding sequence mapping algorithm. Despite a 50-fold increase in database size, pAnno maintains high sensitivity and accuracy in peptide identification and achieves genomic localization of novel events with only

Indexed as

Open Reading FramesProteogenomicsSoftwareAlgorithmsDatabases, ProteinHumansProteomicsWorkflow

Identifiers

PMID42231464
PMCPMC13449647

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.